Carnegie Mellon University

Joint PhD Program in Machine Learning & Public Policy

With the critical importance of addressing global policy problems ranging from disease pandemics to crime and terrorism, and the continuously increasing size and complexity of policy data, the use of machine learning has become essential for data-driven policy analysis and for development of new, practical information technologies that can be directly applied for the public good. The numerous challenges facing our world will require broad, successful innovations at the intersection of machine learning and public policy, to develop novel methods which address critical policy challenges.

The Joint PhD Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop new state-of-the-art machine learning technologies and apply these successfully to real-world policy issues.

This PhD program differs from the ML PhD program in that it places significantly more emphasis on preparation in fields such as economics, organizational behavior, management science, operations research, and substantive policy domains such as health care and crime. Similarly, this program differs from the Public Policy PhD program in its emphasis on machine learning and computer science.

Students in this program will be involved in courses and research from both the Machine Learning Department and the Heinz College. Students are expected both to make fundamental contributions to the science of machine learning as well as addressing core problems in one or more policy domains.

A sample curriculum is as follows:

FALL - 1st Year

SPRING - 1st Year

10-715 Adv. Machine Learning 15-750 Algorithms
36-705 Intermediate Statistics 10-702 Statistical Machine Learning
90-908 Microeconomics Social Science Course
90-901 Heinz PhD Seminar I 90-902 Heinz PhD Seminar II

FALL - Year Two

SPRING - Year Two

Heinz Advanced Elective 15-826 Databases & Data Mining
ML/Stat Advanced Elective ML/PP Advanced Elective
90-918 Heinz PhD Seminar III

Students must complete their first and second Heinz Research Papers by the end of year 2 and year 3 respectively.

Years 3 & 4
Thesis research co-supervised by a faculty in ML and a faculty in the Heinz College.

Applying to the Joint Program in Machine Learning & Public Policy

To apply to the joint program:

Apply to one of the host programs, Heinz or Machine Learning. Heinz Online Application or Machine Learning Online Application
(You may apply separately to each if you wish.)

To be admitted to the Joint Program, the student must be accepted by both Admissions Committees from the Machine Learning Department and Heinz College.